The present invention relates to computer modeling of a system, machine or process. More particularly, the present invention relates to generating an improved nonlinear system model, such as a neural network, as well as using a nonlinear system model or the improved nonlinear system model to generate drive signals as input to a vibration system.
Vibration systems that are capable of simulating loads and/or motions applied to test specimens are generally known. Vibration systems are widely used for performance evaluation, durability tests, and various other purposes as they are highly effective in the development of products. For instance, it is quite common in the development of automobiles, motorcycles, or the like, to subject the vehicle or a substructure thereof to a laboratory environment that simulates operating conditions such as a road or test track. Physical simulation in the laboratory involves a well-known method of data acquisition and analysis in order to develop drive signals that can be applied to the vibration system to reproduce the operating environment. This method includes instrumenting the vehicle with transducers "remote" to the physical inputs of the operating environment. Common remote transducers include, but are not limited to, strain gauges, accelerometers, and displacement sensors, which implicitly define the operating environment of interest. The vehicle is then driven in the same operating environment, while remote transducer responses (internal loads and/or motions) are recorded. During simulation with the vehicle mounted to the vibration system, actuators of the vibration system are driven so as to reproduce the recorded remote transducer responses on the vehicle in the laboratory.
However, before simulated testing can occur, the relationship between the input drive signals to the vibration system and the responses of the remote transducers must be characterized or modeled in the laboratory. This procedure is referred to as "system identification" and involves obtaining a respective linear model or transfer function of the complete physical system (e.g. vibration system, test specimen, and remote transducers) hereinafter referred to as the "physical system" and calculating an inverse system linear model or transfer function of the same. The inverse linear system model or transfer function is then used iteratively to obtain suitable drive signals for the vibration system to obtain substantially the same response from the remote transducers on the test specimen in the laboratory situation as was found in the operating environment.
As those skilled in the art would appreciate, this process of obtaining suitable drive signals is not altered when the remote transducers are not physically remote from the test system inputs (e.g. the case where "remote" transducers are the feedback variables, such as force or motion, of the vibration system controller).
Although the above-described method for modeling and obtaining drive signals for a vibration system has enjoyed substantial success, there is a continuing need to improve such systems. In particular, there is a need to improve the system model and/or the process for obtaining the drive signals. Current techniques for obtaining the drive signals subject the test specimen to loads and displacements during the iterative process to ascertain the drive signals. These initial loads and displacements can damage the test specimen before the intended testing occurs. Accordingly, a method and system that reduces application of "non-testing" loads and displacements in developing drive signals would be desirable.